Face Generation

In this project, you'll use generative adversarial networks to generate new images of faces.

Get the Data

You'll be using two datasets in this project:

  • MNIST
  • CelebA

Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.

If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

In [1]:
data_dir = './data'

# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
Found mnist Data
Found celeba Data

Explore the Data

MNIST

As you're aware, the MNIST dataset contains images of handwritten digits. You can view the first number of examples by changing show_n_images.

In [2]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
Out[2]:
<matplotlib.image.AxesImage at 0x10cf80be0>

CelebA

The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.

In [3]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Out[3]:
<matplotlib.image.AxesImage at 0x10dcba748>

Preprocess the Data

Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).

Build the Neural Network

You'll build the components necessary to build a GANs by implementing the following functions below:

  • model_inputs
  • discriminator
  • generator
  • model_loss
  • model_opt
  • train

Check the Version of TensorFlow and Access to GPU

This will check to make sure you have the correct version of TensorFlow and access to a GPU

In [4]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
TensorFlow Version: 1.1.0
/anaconda/lib/python3.6/site-packages/ipykernel/__main__.py:14: UserWarning: No GPU found. Please use a GPU to train your neural network.

Input

Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:

  • Real input images placeholder with rank 4 using image_width, image_height, and image_channels.
  • Z input placeholder with rank 2 using z_dim.
  • Learning rate placeholder with rank 0.

Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)

In [5]:
import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    inputs_real = tf.placeholder(tf.float32, (None, image_width, image_height, image_channels), name='inputs_real')
    inputs_z = tf.placeholder(tf.float32, (None, z_dim), name='inputs_z')
    learning_rate = tf.placeholder(tf.float32, (), name='learning_rate')
    
    return inputs_real, inputs_z, learning_rate


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Tests Passed

Discriminator

Implement discriminator to create a discriminator neural network that discriminates on images. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).

In [6]:
def discriminator(images, reuse=False):
    """
    Create the discriminator network
    :param images: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """

    alpha=0.2
    x = images
    
    with tf.variable_scope('discriminator', reuse=reuse):

        x1 = tf.layers.conv2d(x, 64, 5, strides=2, padding='same')
        bn1 = tf.layers.batch_normalization(x1, training=True)
        relu1 = tf.maximum(alpha * bn1, bn1)
        
        x2 = tf.layers.conv2d(relu1, 128, 5, strides=2, padding='same')
        bn2 = tf.layers.batch_normalization(x2, training=True)
        relu2 = tf.maximum(alpha * bn2, bn2)
        
        x3 = tf.layers.conv2d(relu2, 256, 5, strides=2, padding='same')
        bn3 = tf.layers.batch_normalization(x3, training=True)
        relu3 = tf.maximum(alpha * bn3, bn3)
        
        flat = tf.reshape(relu3, (-1, 4*4*256))
        logits = tf.layers.dense(flat, 1)
        out = tf.sigmoid(logits)

    return out, logits


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Tests Passed

Generator

Implement generator to generate an image using z. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.

In [17]:
def generator(z, out_channel_dim, is_train=True):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """
    reuse = not is_train
    alpha = 0.2
    
    with tf.variable_scope('generator', reuse=reuse):
        # First fully connected layer
        x1 = tf.layers.dense(z, 4*4*512)
        # Reshape it to start the convolutional stack
        x1 = tf.reshape(x1, (-1, 4, 4, 512))
        x1 = tf.layers.batch_normalization(x1, training=is_train)
        x1 = tf.maximum(alpha * x1, x1)
        # 4x4x512 now

        x2 = tf.layers.conv2d_transpose(x1, 256, 4, strides=1, padding='valid')
        x2 = tf.layers.batch_normalization(x2, training=is_train)
        x2 = tf.maximum(alpha * x2, x2)
        # 7x7x256 now

        x3 = tf.layers.conv2d_transpose(x2, 128, 4, strides=2, padding='same')
        x3 = tf.layers.batch_normalization(x3, training=is_train)
        x3 = tf.maximum(alpha * x3, x3)
        # 14x14x128 now

        # Output layer
        logits = tf.layers.conv2d_transpose(x3, out_channel_dim, 4, strides=2, padding='same')
        
        out = tf.tanh(logits)
    
    return out


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Tests Passed

Loss

Implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:

  • discriminator(images, reuse=False)
  • generator(z, out_channel_dim, is_train=True)
In [18]:
def model_loss(input_real, input_z, out_channel_dim):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """
    smooth = 0.1
    g_model = generator(input_z, out_channel_dim)
    d_model_real, d_logits_real = discriminator(input_real)
    d_model_fake, d_logits_fake = discriminator(g_model, reuse=True)

    d_loss_real = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, labels=tf.ones_like(d_model_real) * (1 - smooth)))
    d_loss_fake = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.zeros_like(d_model_fake)))
    d_loss = d_loss_real + d_loss_fake
    
    g_loss = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.ones_like(d_model_fake)))
    
    return d_loss, g_loss


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Tests Passed

Optimization

Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).

In [19]:
def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
    
    # Get weights and bias to update
    t_vars = tf.trainable_variables()
    d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
    g_vars = [var for var in t_vars if var.name.startswith('generator')]

    # Optimize
    all_update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
    
    g_update_ops = [var for var in all_update_ops if var.name.startswith('generator')]
    d_update_ops = [var for var in all_update_ops if var.name.startswith('discriminator')]

    with tf.control_dependencies(d_update_ops):
        d_train_opt = tf.train.AdamOptimizer(learning_rate,beta1=beta1).minimize(d_loss, var_list=d_vars)
    with tf.control_dependencies(g_update_ops):
        g_train_opt = tf.train.AdamOptimizer(learning_rate,beta1=beta1).minimize(g_loss, var_list=g_vars)
    return d_train_opt, g_train_opt


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
Tests Passed

Neural Network Training

Show Output

Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training.

In [20]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()

Train

Implement train to build and train the GANs. Use the following functions you implemented:

  • model_inputs(image_width, image_height, image_channels, z_dim)
  • model_loss(input_real, input_z, out_channel_dim)
  • model_opt(d_loss, g_loss, learning_rate, beta1)

Use the show_generator_output to show generator output while you train. Running show_generator_output for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator output every 100 batches.

In [45]:
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """
    
    inputs_real, inputs_z, lr = model_inputs(data_shape[1], data_shape[2], data_shape[3], z_dim)
    d_loss, g_loss = model_loss(inputs_real, inputs_z, data_shape[-1])
    d_train_opt, g_train_opt = model_opt(d_loss, g_loss, learning_rate, beta1)
    
    steps = 0
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for epoch_i in range(epoch_count):
            for batch_images in get_batches(batch_size):
                steps += 1
                
                # Sample random noise for G
                batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))
                                
                # The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5
                #  of 28x28 dimensional images.
                batch_images = batch_images * 2
                
                # Run optimizers
                _ = sess.run(d_train_opt, feed_dict={inputs_real: batch_images, inputs_z: batch_z, lr:learning_rate})
                _ = sess.run(g_train_opt, feed_dict={inputs_z: batch_z, lr:learning_rate})
                
                
                if steps % 10 == 0:
                    # At the end of each epoch, get the losses and print them out                    
                    train_loss_d = d_loss.eval({inputs_z:batch_z, inputs_real: batch_images})
                    train_loss_g = g_loss.eval({inputs_z:batch_z})

                    print("Epoch {}/{} Step {}...".format(epoch_i+1, epoch_count, steps),
                          "Discriminator Loss: {:.4f}...".format(train_loss_d),
                          "Generator Loss: {:.4f}".format(train_loss_g))
                    
                    show_generator_output(sess, 25, inputs_z, data_shape[3], data_image_mode)

MNIST

Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.

In [47]:
batch_size = 100
z_dim = 100
learning_rate = 0.001
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)
Epoch 1/2 Step 10... Discriminator Loss: 0.5029... Generator Loss: 13.6718
Epoch 1/2 Step 20... Discriminator Loss: 0.3973... Generator Loss: 5.9200
Epoch 1/2 Step 30... Discriminator Loss: 0.5920... Generator Loss: 1.7984
Epoch 1/2 Step 40... Discriminator Loss: 2.4719... Generator Loss: 0.8993
Epoch 1/2 Step 50... Discriminator Loss: 1.9581... Generator Loss: 1.2382
Epoch 1/2 Step 60... Discriminator Loss: 0.8175... Generator Loss: 2.3905
Epoch 1/2 Step 70... Discriminator Loss: 0.4747... Generator Loss: 2.8480
Epoch 1/2 Step 80... Discriminator Loss: 0.6375... Generator Loss: 2.5280
Epoch 1/2 Step 90... Discriminator Loss: 1.4505... Generator Loss: 0.7230
Epoch 1/2 Step 100... Discriminator Loss: 0.8829... Generator Loss: 1.4267
Epoch 1/2 Step 110... Discriminator Loss: 1.7353... Generator Loss: 0.4469
Epoch 1/2 Step 120... Discriminator Loss: 0.8397... Generator Loss: 2.5694
Epoch 1/2 Step 130... Discriminator Loss: 0.9618... Generator Loss: 1.3167
Epoch 1/2 Step 140... Discriminator Loss: 0.9219... Generator Loss: 1.9338
Epoch 1/2 Step 150... Discriminator Loss: 1.0883... Generator Loss: 1.5879
Epoch 1/2 Step 160... Discriminator Loss: 0.7028... Generator Loss: 2.3294
Epoch 1/2 Step 170... Discriminator Loss: 0.9315... Generator Loss: 1.2033
Epoch 1/2 Step 180... Discriminator Loss: 0.9793... Generator Loss: 1.6658
Epoch 1/2 Step 190... Discriminator Loss: 0.9317... Generator Loss: 1.7269
Epoch 1/2 Step 200... Discriminator Loss: 0.8364... Generator Loss: 1.3814
Epoch 1/2 Step 210... Discriminator Loss: 1.0986... Generator Loss: 1.4712
Epoch 1/2 Step 220... Discriminator Loss: 1.1386... Generator Loss: 1.7293
Epoch 1/2 Step 230... Discriminator Loss: 1.6057... Generator Loss: 0.5020
Epoch 1/2 Step 240... Discriminator Loss: 0.9902... Generator Loss: 1.3689
Epoch 1/2 Step 250... Discriminator Loss: 0.9861... Generator Loss: 1.7618
Epoch 1/2 Step 260... Discriminator Loss: 1.0221... Generator Loss: 1.2660
Epoch 1/2 Step 270... Discriminator Loss: 1.1284... Generator Loss: 0.8141
Epoch 1/2 Step 280... Discriminator Loss: 0.9673... Generator Loss: 1.6190
Epoch 1/2 Step 290... Discriminator Loss: 0.9445... Generator Loss: 1.1910
Epoch 1/2 Step 300... Discriminator Loss: 1.2023... Generator Loss: 1.0230
Epoch 1/2 Step 310... Discriminator Loss: 1.0813... Generator Loss: 1.8403
Epoch 1/2 Step 320... Discriminator Loss: 0.9771... Generator Loss: 1.1731
Epoch 1/2 Step 330... Discriminator Loss: 1.3251... Generator Loss: 0.6853
Epoch 1/2 Step 340... Discriminator Loss: 1.3396... Generator Loss: 1.9380
Epoch 1/2 Step 350... Discriminator Loss: 0.9792... Generator Loss: 1.0845
Epoch 1/2 Step 360... Discriminator Loss: 1.1339... Generator Loss: 1.4109
Epoch 1/2 Step 370... Discriminator Loss: 0.9979... Generator Loss: 1.6021
Epoch 1/2 Step 380... Discriminator Loss: 0.8663... Generator Loss: 1.5250
Epoch 1/2 Step 390... Discriminator Loss: 1.2672... Generator Loss: 0.7346
Epoch 1/2 Step 400... Discriminator Loss: 1.2299... Generator Loss: 0.7660
Epoch 1/2 Step 410... Discriminator Loss: 1.1422... Generator Loss: 1.5220
Epoch 1/2 Step 420... Discriminator Loss: 1.0799... Generator Loss: 2.3103
Epoch 1/2 Step 430... Discriminator Loss: 1.0819... Generator Loss: 0.9732
Epoch 1/2 Step 440... Discriminator Loss: 1.0948... Generator Loss: 1.0049
Epoch 1/2 Step 450... Discriminator Loss: 1.2269... Generator Loss: 1.4512
Epoch 1/2 Step 460... Discriminator Loss: 1.2752... Generator Loss: 0.6444
Epoch 1/2 Step 470... Discriminator Loss: 1.1465... Generator Loss: 0.7171
Epoch 1/2 Step 480... Discriminator Loss: 1.0921... Generator Loss: 2.1928
Epoch 1/2 Step 490... Discriminator Loss: 0.9817... Generator Loss: 1.1282
Epoch 1/2 Step 500... Discriminator Loss: 1.4311... Generator Loss: 0.5270
Epoch 1/2 Step 510... Discriminator Loss: 1.8634... Generator Loss: 3.0513
Epoch 1/2 Step 520... Discriminator Loss: 1.0245... Generator Loss: 0.9661
Epoch 1/2 Step 530... Discriminator Loss: 1.1026... Generator Loss: 0.7821
Epoch 1/2 Step 540... Discriminator Loss: 1.1663... Generator Loss: 2.1899
Epoch 1/2 Step 550... Discriminator Loss: 0.9132... Generator Loss: 1.2958
Epoch 1/2 Step 560... Discriminator Loss: 1.5713... Generator Loss: 2.3580
Epoch 1/2 Step 570... Discriminator Loss: 1.2366... Generator Loss: 1.1321
Epoch 1/2 Step 580... Discriminator Loss: 0.9463... Generator Loss: 1.2441
Epoch 1/2 Step 590... Discriminator Loss: 1.8268... Generator Loss: 2.5299
Epoch 1/2 Step 600... Discriminator Loss: 0.9875... Generator Loss: 1.1532
Epoch 2/2 Step 610... Discriminator Loss: 1.1414... Generator Loss: 1.0308
Epoch 2/2 Step 620... Discriminator Loss: 1.1135... Generator Loss: 1.3415
Epoch 2/2 Step 630... Discriminator Loss: 1.0629... Generator Loss: 0.7965
Epoch 2/2 Step 640... Discriminator Loss: 1.3967... Generator Loss: 2.4287
Epoch 2/2 Step 650... Discriminator Loss: 1.0435... Generator Loss: 1.0152
Epoch 2/2 Step 660... Discriminator Loss: 1.0506... Generator Loss: 1.0989
Epoch 2/2 Step 670... Discriminator Loss: 1.1918... Generator Loss: 1.7962
Epoch 2/2 Step 680... Discriminator Loss: 0.9899... Generator Loss: 0.9200
Epoch 2/2 Step 690... Discriminator Loss: 0.9020... Generator Loss: 1.5624
Epoch 2/2 Step 700... Discriminator Loss: 1.0518... Generator Loss: 1.0864
Epoch 2/2 Step 710... Discriminator Loss: 0.9420... Generator Loss: 1.3373
Epoch 2/2 Step 720... Discriminator Loss: 1.2422... Generator Loss: 1.8534
Epoch 2/2 Step 730... Discriminator Loss: 1.0676... Generator Loss: 0.9498
Epoch 2/2 Step 740... Discriminator Loss: 1.1642... Generator Loss: 1.0696
Epoch 2/2 Step 750... Discriminator Loss: 0.9153... Generator Loss: 1.1272
Epoch 2/2 Step 760... Discriminator Loss: 0.7707... Generator Loss: 1.5184
Epoch 2/2 Step 770... Discriminator Loss: 1.2855... Generator Loss: 1.2936
Epoch 2/2 Step 780... Discriminator Loss: 1.3050... Generator Loss: 1.6306
Epoch 2/2 Step 790... Discriminator Loss: 1.0949... Generator Loss: 0.9207
Epoch 2/2 Step 800... Discriminator Loss: 1.5033... Generator Loss: 0.4991
Epoch 2/2 Step 810... Discriminator Loss: 0.8424... Generator Loss: 1.2358
Epoch 2/2 Step 820... Discriminator Loss: 0.9984... Generator Loss: 0.9049
Epoch 2/2 Step 830... Discriminator Loss: 2.2901... Generator Loss: 3.9800
Epoch 2/2 Step 840... Discriminator Loss: 0.9888... Generator Loss: 0.8996
Epoch 2/2 Step 850... Discriminator Loss: 0.9381... Generator Loss: 1.7068
Epoch 2/2 Step 860... Discriminator Loss: 0.9388... Generator Loss: 1.0107
Epoch 2/2 Step 870... Discriminator Loss: 1.3905... Generator Loss: 2.5204
Epoch 2/2 Step 880... Discriminator Loss: 0.8387... Generator Loss: 1.2119
Epoch 2/2 Step 890... Discriminator Loss: 1.0102... Generator Loss: 0.9373
Epoch 2/2 Step 900... Discriminator Loss: 1.0846... Generator Loss: 0.9144
Epoch 2/2 Step 910... Discriminator Loss: 0.8437... Generator Loss: 1.7718
Epoch 2/2 Step 920... Discriminator Loss: 1.3475... Generator Loss: 0.5233
Epoch 2/2 Step 930... Discriminator Loss: 0.9462... Generator Loss: 0.9331
Epoch 2/2 Step 940... Discriminator Loss: 1.5732... Generator Loss: 2.9082
Epoch 2/2 Step 950... Discriminator Loss: 0.8093... Generator Loss: 1.3067
Epoch 2/2 Step 960... Discriminator Loss: 0.9897... Generator Loss: 0.9121
Epoch 2/2 Step 970... Discriminator Loss: 1.0493... Generator Loss: 0.8296
Epoch 2/2 Step 980... Discriminator Loss: 0.8968... Generator Loss: 2.3771
Epoch 2/2 Step 990... Discriminator Loss: 0.9293... Generator Loss: 1.0723
Epoch 2/2 Step 1000... Discriminator Loss: 0.9841... Generator Loss: 0.9051
Epoch 2/2 Step 1010... Discriminator Loss: 0.7083... Generator Loss: 1.3982
Epoch 2/2 Step 1020... Discriminator Loss: 0.9425... Generator Loss: 1.1928
Epoch 2/2 Step 1030... Discriminator Loss: 1.0746... Generator Loss: 0.8555
Epoch 2/2 Step 1040... Discriminator Loss: 0.7718... Generator Loss: 1.6931
Epoch 2/2 Step 1050... Discriminator Loss: 0.7369... Generator Loss: 1.4300
Epoch 2/2 Step 1060... Discriminator Loss: 1.0232... Generator Loss: 1.4694
Epoch 2/2 Step 1070... Discriminator Loss: 0.9011... Generator Loss: 1.1697
Epoch 2/2 Step 1080... Discriminator Loss: 0.8162... Generator Loss: 1.5539
Epoch 2/2 Step 1090... Discriminator Loss: 0.7879... Generator Loss: 1.2277
Epoch 2/2 Step 1100... Discriminator Loss: 2.6459... Generator Loss: 0.2164
Epoch 2/2 Step 1110... Discriminator Loss: 0.7888... Generator Loss: 1.3165
Epoch 2/2 Step 1120... Discriminator Loss: 0.8585... Generator Loss: 1.7816
Epoch 2/2 Step 1130... Discriminator Loss: 0.8062... Generator Loss: 1.1912
Epoch 2/2 Step 1140... Discriminator Loss: 1.2256... Generator Loss: 0.8366
Epoch 2/2 Step 1150... Discriminator Loss: 2.6555... Generator Loss: 5.7804
Epoch 2/2 Step 1160... Discriminator Loss: 1.2322... Generator Loss: 2.2005
Epoch 2/2 Step 1170... Discriminator Loss: 0.8961... Generator Loss: 1.8141
Epoch 2/2 Step 1180... Discriminator Loss: 0.7280... Generator Loss: 1.3530
Epoch 2/2 Step 1190... Discriminator Loss: 1.0342... Generator Loss: 0.8049
Epoch 2/2 Step 1200... Discriminator Loss: 0.6877... Generator Loss: 1.5958

CelebA

Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.

In [49]:
batch_size = 100
z_dim = 100
learning_rate = 0.001
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)
Epoch 1/1 Step 10... Discriminator Loss: 1.0662... Generator Loss: 0.9333
Epoch 1/1 Step 20... Discriminator Loss: 0.4710... Generator Loss: 8.0722
Epoch 1/1 Step 30... Discriminator Loss: 1.2519... Generator Loss: 0.5990
Epoch 1/1 Step 40... Discriminator Loss: 0.5316... Generator Loss: 6.8169
Epoch 1/1 Step 50... Discriminator Loss: 0.4841... Generator Loss: 9.6191
Epoch 1/1 Step 60... Discriminator Loss: 0.4989... Generator Loss: 5.8106
Epoch 1/1 Step 70... Discriminator Loss: 0.8144... Generator Loss: 1.4364
Epoch 1/1 Step 80... Discriminator Loss: 0.6517... Generator Loss: 2.1403
Epoch 1/1 Step 90... Discriminator Loss: 1.0426... Generator Loss: 1.2611
Epoch 1/1 Step 100... Discriminator Loss: 0.8652... Generator Loss: 6.5052
Epoch 1/1 Step 110... Discriminator Loss: 1.8030... Generator Loss: 6.4883
Epoch 1/1 Step 120... Discriminator Loss: 0.8502... Generator Loss: 1.2984
Epoch 1/1 Step 130... Discriminator Loss: 0.8158... Generator Loss: 1.5028
Epoch 1/1 Step 140... Discriminator Loss: 0.6846... Generator Loss: 2.1801
Epoch 1/1 Step 150... Discriminator Loss: 1.6317... Generator Loss: 1.6608
Epoch 1/1 Step 160... Discriminator Loss: 1.1195... Generator Loss: 0.9082
Epoch 1/1 Step 170... Discriminator Loss: 0.9379... Generator Loss: 0.9944
Epoch 1/1 Step 180... Discriminator Loss: 1.5938... Generator Loss: 0.6079
Epoch 1/1 Step 190... Discriminator Loss: 1.3783... Generator Loss: 0.7598
Epoch 1/1 Step 200... Discriminator Loss: 0.8252... Generator Loss: 2.1521
Epoch 1/1 Step 210... Discriminator Loss: 1.4325... Generator Loss: 0.5273
Epoch 1/1 Step 220... Discriminator Loss: 1.7940... Generator Loss: 3.1527
Epoch 1/1 Step 230... Discriminator Loss: 1.1512... Generator Loss: 0.7887
Epoch 1/1 Step 240... Discriminator Loss: 1.3756... Generator Loss: 1.1264
Epoch 1/1 Step 250... Discriminator Loss: 1.3623... Generator Loss: 0.6425
Epoch 1/1 Step 260... Discriminator Loss: 1.1005... Generator Loss: 0.8126
Epoch 1/1 Step 270... Discriminator Loss: 1.5906... Generator Loss: 0.4366
Epoch 1/1 Step 280... Discriminator Loss: 1.0297... Generator Loss: 1.6200
Epoch 1/1 Step 290... Discriminator Loss: 1.1409... Generator Loss: 0.8372
Epoch 1/1 Step 300... Discriminator Loss: 1.0693... Generator Loss: 1.8248
Epoch 1/1 Step 310... Discriminator Loss: 0.6934... Generator Loss: 1.5171
Epoch 1/1 Step 320... Discriminator Loss: 1.1087... Generator Loss: 1.5110
Epoch 1/1 Step 330... Discriminator Loss: 1.3329... Generator Loss: 0.5326
Epoch 1/1 Step 340... Discriminator Loss: 1.4590... Generator Loss: 0.4990
Epoch 1/1 Step 350... Discriminator Loss: 1.1758... Generator Loss: 0.7552
Epoch 1/1 Step 360... Discriminator Loss: 0.8156... Generator Loss: 1.1982
Epoch 1/1 Step 370... Discriminator Loss: 1.1987... Generator Loss: 0.7246
Epoch 1/1 Step 380... Discriminator Loss: 0.6832... Generator Loss: 2.1742
Epoch 1/1 Step 390... Discriminator Loss: 1.6431... Generator Loss: 3.3756
Epoch 1/1 Step 400... Discriminator Loss: 0.8967... Generator Loss: 2.3631
Epoch 1/1 Step 410... Discriminator Loss: 0.7733... Generator Loss: 1.7964
Epoch 1/1 Step 420... Discriminator Loss: 0.9838... Generator Loss: 0.9600
Epoch 1/1 Step 430... Discriminator Loss: 0.7496... Generator Loss: 1.4385
Epoch 1/1 Step 440... Discriminator Loss: 1.1735... Generator Loss: 0.7710
Epoch 1/1 Step 450... Discriminator Loss: 1.1391... Generator Loss: 2.9369
Epoch 1/1 Step 460... Discriminator Loss: 1.1900... Generator Loss: 0.7251
Epoch 1/1 Step 470... Discriminator Loss: 0.8822... Generator Loss: 1.4142
Epoch 1/1 Step 480... Discriminator Loss: 1.1693... Generator Loss: 1.8320
Epoch 1/1 Step 490... Discriminator Loss: 1.4422... Generator Loss: 0.5776
Epoch 1/1 Step 500... Discriminator Loss: 1.1166... Generator Loss: 1.4179
Epoch 1/1 Step 510... Discriminator Loss: 1.4795... Generator Loss: 0.5241
Epoch 1/1 Step 520... Discriminator Loss: 1.1272... Generator Loss: 0.7478
Epoch 1/1 Step 530... Discriminator Loss: 0.6836... Generator Loss: 2.0313
Epoch 1/1 Step 540... Discriminator Loss: 1.1549... Generator Loss: 1.2163
Epoch 1/1 Step 550... Discriminator Loss: 1.1929... Generator Loss: 1.1154
Epoch 1/1 Step 560... Discriminator Loss: 1.3473... Generator Loss: 1.3986
Epoch 1/1 Step 570... Discriminator Loss: 1.2443... Generator Loss: 0.6722
Epoch 1/1 Step 580... Discriminator Loss: 1.0852... Generator Loss: 0.7773
Epoch 1/1 Step 590... Discriminator Loss: 0.7764... Generator Loss: 1.4918
Epoch 1/1 Step 600... Discriminator Loss: 1.0070... Generator Loss: 1.2919
Epoch 1/1 Step 610... Discriminator Loss: 1.5802... Generator Loss: 2.8311
Epoch 1/1 Step 620... Discriminator Loss: 1.6063... Generator Loss: 0.3928
Epoch 1/1 Step 630... Discriminator Loss: 1.4186... Generator Loss: 0.5223
Epoch 1/1 Step 640... Discriminator Loss: 1.0461... Generator Loss: 1.9804
Epoch 1/1 Step 650... Discriminator Loss: 1.8014... Generator Loss: 2.9036
Epoch 1/1 Step 660... Discriminator Loss: 1.1472... Generator Loss: 0.9507
Epoch 1/1 Step 670... Discriminator Loss: 1.0795... Generator Loss: 1.5330
Epoch 1/1 Step 680... Discriminator Loss: 0.9180... Generator Loss: 2.7315
Epoch 1/1 Step 690... Discriminator Loss: 0.8875... Generator Loss: 1.0820
Epoch 1/1 Step 700... Discriminator Loss: 1.2643... Generator Loss: 0.6449
Epoch 1/1 Step 710... Discriminator Loss: 0.9654... Generator Loss: 1.5018
Epoch 1/1 Step 720... Discriminator Loss: 0.9354... Generator Loss: 1.0759
Epoch 1/1 Step 730... Discriminator Loss: 1.3564... Generator Loss: 0.5769
Epoch 1/1 Step 740... Discriminator Loss: 0.7153... Generator Loss: 2.0666
Epoch 1/1 Step 750... Discriminator Loss: 0.8691... Generator Loss: 1.0953
Epoch 1/1 Step 760... Discriminator Loss: 1.2673... Generator Loss: 2.7935
Epoch 1/1 Step 770... Discriminator Loss: 0.9401... Generator Loss: 1.2765
Epoch 1/1 Step 780... Discriminator Loss: 0.8334... Generator Loss: 1.5569
Epoch 1/1 Step 790... Discriminator Loss: 1.0103... Generator Loss: 0.9642
Epoch 1/1 Step 800... Discriminator Loss: 0.8742... Generator Loss: 2.3721
Epoch 1/1 Step 810... Discriminator Loss: 0.9334... Generator Loss: 1.1510
Epoch 1/1 Step 820... Discriminator Loss: 1.5314... Generator Loss: 3.0153
Epoch 1/1 Step 830... Discriminator Loss: 1.9747... Generator Loss: 4.1049
Epoch 1/1 Step 840... Discriminator Loss: 0.8551... Generator Loss: 1.1490
Epoch 1/1 Step 850... Discriminator Loss: 0.7759... Generator Loss: 1.4773
Epoch 1/1 Step 860... Discriminator Loss: 0.9907... Generator Loss: 2.3366
Epoch 1/1 Step 870... Discriminator Loss: 1.4034... Generator Loss: 0.5601
Epoch 1/1 Step 880... Discriminator Loss: 1.2777... Generator Loss: 0.6734
Epoch 1/1 Step 890... Discriminator Loss: 1.1092... Generator Loss: 1.2947
Epoch 1/1 Step 900... Discriminator Loss: 1.1198... Generator Loss: 0.7691
Epoch 1/1 Step 910... Discriminator Loss: 1.3848... Generator Loss: 0.5594
Epoch 1/1 Step 920... Discriminator Loss: 1.3874... Generator Loss: 2.5465
Epoch 1/1 Step 930... Discriminator Loss: 1.0317... Generator Loss: 1.7669
Epoch 1/1 Step 940... Discriminator Loss: 0.9671... Generator Loss: 2.3662
Epoch 1/1 Step 950... Discriminator Loss: 0.7987... Generator Loss: 1.4621
Epoch 1/1 Step 960... Discriminator Loss: 1.2279... Generator Loss: 0.6691
Epoch 1/1 Step 970... Discriminator Loss: 1.7732... Generator Loss: 2.3674
Epoch 1/1 Step 980... Discriminator Loss: 1.2205... Generator Loss: 0.6849
Epoch 1/1 Step 990... Discriminator Loss: 1.0079... Generator Loss: 1.7429
Epoch 1/1 Step 1000... Discriminator Loss: 0.5750... Generator Loss: 2.2769
Epoch 1/1 Step 1010... Discriminator Loss: 0.9709... Generator Loss: 0.9592
Epoch 1/1 Step 1020... Discriminator Loss: 1.3157... Generator Loss: 2.0148
Epoch 1/1 Step 1030... Discriminator Loss: 0.6975... Generator Loss: 2.0588
Epoch 1/1 Step 1040... Discriminator Loss: 1.2369... Generator Loss: 2.4991
Epoch 1/1 Step 1050... Discriminator Loss: 1.4361... Generator Loss: 0.5092
Epoch 1/1 Step 1060... Discriminator Loss: 1.2937... Generator Loss: 0.5872
Epoch 1/1 Step 1070... Discriminator Loss: 0.6897... Generator Loss: 1.8449
Epoch 1/1 Step 1080... Discriminator Loss: 1.5064... Generator Loss: 2.3134
Epoch 1/1 Step 1090... Discriminator Loss: 0.7766... Generator Loss: 1.6046
Epoch 1/1 Step 1100... Discriminator Loss: 0.8541... Generator Loss: 1.1360
Epoch 1/1 Step 1110... Discriminator Loss: 1.4874... Generator Loss: 2.9122
Epoch 1/1 Step 1120... Discriminator Loss: 0.9048... Generator Loss: 0.9746
Epoch 1/1 Step 1130... Discriminator Loss: 0.7430... Generator Loss: 2.5944
Epoch 1/1 Step 1140... Discriminator Loss: 0.8389... Generator Loss: 1.4174
Epoch 1/1 Step 1150... Discriminator Loss: 0.8672... Generator Loss: 1.2356
Epoch 1/1 Step 1160... Discriminator Loss: 1.4238... Generator Loss: 0.5301
Epoch 1/1 Step 1170... Discriminator Loss: 0.9792... Generator Loss: 1.3315
Epoch 1/1 Step 1180... Discriminator Loss: 0.6582... Generator Loss: 2.0691
Epoch 1/1 Step 1190... Discriminator Loss: 1.1754... Generator Loss: 0.7003
Epoch 1/1 Step 1200... Discriminator Loss: 0.9298... Generator Loss: 1.8042
Epoch 1/1 Step 1210... Discriminator Loss: 0.9207... Generator Loss: 2.2787
Epoch 1/1 Step 1220... Discriminator Loss: 0.6702... Generator Loss: 2.0868
Epoch 1/1 Step 1230... Discriminator Loss: 0.8679... Generator Loss: 1.1492
Epoch 1/1 Step 1240... Discriminator Loss: 0.8736... Generator Loss: 1.1377
Epoch 1/1 Step 1250... Discriminator Loss: 1.1049... Generator Loss: 2.3545
Epoch 1/1 Step 1260... Discriminator Loss: 0.8737... Generator Loss: 1.0578
Epoch 1/1 Step 1270... Discriminator Loss: 2.5207... Generator Loss: 2.6817
Epoch 1/1 Step 1280... Discriminator Loss: 1.1538... Generator Loss: 0.7844
Epoch 1/1 Step 1290... Discriminator Loss: 1.5440... Generator Loss: 0.4654
Epoch 1/1 Step 1300... Discriminator Loss: 1.3547... Generator Loss: 0.5909
Epoch 1/1 Step 1310... Discriminator Loss: 0.6100... Generator Loss: 2.4001
Epoch 1/1 Step 1320... Discriminator Loss: 0.8910... Generator Loss: 1.6205
Epoch 1/1 Step 1330... Discriminator Loss: 0.5545... Generator Loss: 2.3675
Epoch 1/1 Step 1340... Discriminator Loss: 0.7368... Generator Loss: 1.8452
Epoch 1/1 Step 1350... Discriminator Loss: 1.8149... Generator Loss: 0.3552
Epoch 1/1 Step 1360... Discriminator Loss: 0.9467... Generator Loss: 1.3863
Epoch 1/1 Step 1370... Discriminator Loss: 0.9125... Generator Loss: 0.9744
Epoch 1/1 Step 1380... Discriminator Loss: 0.9154... Generator Loss: 1.0525
Epoch 1/1 Step 1390... Discriminator Loss: 0.6608... Generator Loss: 2.0817
Epoch 1/1 Step 1400... Discriminator Loss: 1.5598... Generator Loss: 0.4318
Epoch 1/1 Step 1410... Discriminator Loss: 1.0298... Generator Loss: 0.8719
Epoch 1/1 Step 1420... Discriminator Loss: 0.9060... Generator Loss: 2.6196
Epoch 1/1 Step 1430... Discriminator Loss: 0.5813... Generator Loss: 2.5208
Epoch 1/1 Step 1440... Discriminator Loss: 0.5509... Generator Loss: 3.5074
Epoch 1/1 Step 1450... Discriminator Loss: 0.6026... Generator Loss: 2.3458
Epoch 1/1 Step 1460... Discriminator Loss: 0.6166... Generator Loss: 2.5253
Epoch 1/1 Step 1470... Discriminator Loss: 1.5271... Generator Loss: 0.4783
Epoch 1/1 Step 1480... Discriminator Loss: 1.0355... Generator Loss: 2.3118
Epoch 1/1 Step 1490... Discriminator Loss: 1.1945... Generator Loss: 0.7141
Epoch 1/1 Step 1500... Discriminator Loss: 1.0160... Generator Loss: 1.3243
Epoch 1/1 Step 1510... Discriminator Loss: 0.9321... Generator Loss: 1.0400
Epoch 1/1 Step 1520... Discriminator Loss: 0.6472... Generator Loss: 3.5019
Epoch 1/1 Step 1530... Discriminator Loss: 1.0438... Generator Loss: 2.4144
Epoch 1/1 Step 1540... Discriminator Loss: 0.6579... Generator Loss: 2.1385
Epoch 1/1 Step 1550... Discriminator Loss: 1.0368... Generator Loss: 2.5397
Epoch 1/1 Step 1560... Discriminator Loss: 1.2672... Generator Loss: 3.2163
Epoch 1/1 Step 1570... Discriminator Loss: 0.6720... Generator Loss: 2.3260
Epoch 1/1 Step 1580... Discriminator Loss: 1.1175... Generator Loss: 1.7480
Epoch 1/1 Step 1590... Discriminator Loss: 1.0863... Generator Loss: 0.8670
Epoch 1/1 Step 1600... Discriminator Loss: 0.7187... Generator Loss: 2.0223
Epoch 1/1 Step 1610... Discriminator Loss: 0.7807... Generator Loss: 1.2717
Epoch 1/1 Step 1620... Discriminator Loss: 0.9942... Generator Loss: 0.9817
Epoch 1/1 Step 1630... Discriminator Loss: 2.3093... Generator Loss: 0.2638
Epoch 1/1 Step 1640... Discriminator Loss: 0.8892... Generator Loss: 1.8993
Epoch 1/1 Step 1650... Discriminator Loss: 1.2781... Generator Loss: 2.9494
Epoch 1/1 Step 1660... Discriminator Loss: 0.7163... Generator Loss: 1.3283
Epoch 1/1 Step 1670... Discriminator Loss: 0.6817... Generator Loss: 2.3066
Epoch 1/1 Step 1680... Discriminator Loss: 0.4798... Generator Loss: 3.0765
Epoch 1/1 Step 1690... Discriminator Loss: 0.8843... Generator Loss: 1.6644
Epoch 1/1 Step 1700... Discriminator Loss: 1.5822... Generator Loss: 0.4775
Epoch 1/1 Step 1710... Discriminator Loss: 0.9793... Generator Loss: 3.1678
Epoch 1/1 Step 1720... Discriminator Loss: 0.8928... Generator Loss: 1.0619
Epoch 1/1 Step 1730... Discriminator Loss: 0.7811... Generator Loss: 1.6186
Epoch 1/1 Step 1740... Discriminator Loss: 0.6816... Generator Loss: 1.7363
Epoch 1/1 Step 1750... Discriminator Loss: 0.6625... Generator Loss: 2.4025
Epoch 1/1 Step 1760... Discriminator Loss: 0.8413... Generator Loss: 2.4573
Epoch 1/1 Step 1770... Discriminator Loss: 0.9105... Generator Loss: 1.0223
Epoch 1/1 Step 1780... Discriminator Loss: 0.4765... Generator Loss: 3.0522
Epoch 1/1 Step 1790... Discriminator Loss: 0.7783... Generator Loss: 4.1307
Epoch 1/1 Step 1800... Discriminator Loss: 0.5467... Generator Loss: 2.3312
Epoch 1/1 Step 1810... Discriminator Loss: 0.8156... Generator Loss: 1.2552
Epoch 1/1 Step 1820... Discriminator Loss: 1.1201... Generator Loss: 0.7206
Epoch 1/1 Step 1830... Discriminator Loss: 0.8011... Generator Loss: 1.2555
Epoch 1/1 Step 1840... Discriminator Loss: 0.9337... Generator Loss: 2.1984
Epoch 1/1 Step 1850... Discriminator Loss: 0.9094... Generator Loss: 1.6737
Epoch 1/1 Step 1860... Discriminator Loss: 1.0459... Generator Loss: 0.8318
Epoch 1/1 Step 1870... Discriminator Loss: 1.3875... Generator Loss: 1.8137
Epoch 1/1 Step 1880... Discriminator Loss: 0.8131... Generator Loss: 1.2761
Epoch 1/1 Step 1890... Discriminator Loss: 0.5548... Generator Loss: 2.1789
Epoch 1/1 Step 1900... Discriminator Loss: 0.5743... Generator Loss: 2.6945
Epoch 1/1 Step 1910... Discriminator Loss: 0.5519... Generator Loss: 3.5334
Epoch 1/1 Step 1920... Discriminator Loss: 0.5416... Generator Loss: 2.1047
Epoch 1/1 Step 1930... Discriminator Loss: 1.6584... Generator Loss: 0.4358
Epoch 1/1 Step 1940... Discriminator Loss: 0.8329... Generator Loss: 1.4907
Epoch 1/1 Step 1950... Discriminator Loss: 1.3835... Generator Loss: 2.9829
Epoch 1/1 Step 1960... Discriminator Loss: 0.7094... Generator Loss: 2.5331
Epoch 1/1 Step 1970... Discriminator Loss: 0.5221... Generator Loss: 2.2456
Epoch 1/1 Step 1980... Discriminator Loss: 0.6276... Generator Loss: 1.8743
Epoch 1/1 Step 1990... Discriminator Loss: 0.4719... Generator Loss: 2.7153
Epoch 1/1 Step 2000... Discriminator Loss: 0.5260... Generator Loss: 2.7526
Epoch 1/1 Step 2010... Discriminator Loss: 0.6829... Generator Loss: 1.6492
Epoch 1/1 Step 2020... Discriminator Loss: 0.8088... Generator Loss: 2.7573

Submitting This Project

When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.